{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.tensor([[1, 2, 3], [ 4,  5,  6]], dtype=torch.double)\n",
    "y = torch.tensor([[7, 8, 9], [10, 11, 12]], dtype=torch.double)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "softmax = nn.Softmax(dim=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[1., 2., 3.],\n",
      "        [4., 5., 6.]], dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor([[0.0900, 0.2447, 0.6652],\n",
       "        [0.0900, 0.2447, 0.6652]], dtype=torch.float64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(x)\n",
    "softmax(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[  2.7183,   7.3891,  20.0855],\n",
      "        [ 54.5982, 148.4132, 403.4288]], dtype=torch.float64)\n",
      "tensor([ 30.1929, 606.4401], dtype=torch.float64)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensor(0.0900, dtype=torch.float64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x1 = torch.exp(x)\n",
    "print(x1)\n",
    "x1_sum = torch.sum(x1, dim=1)\n",
    "print(x1_sum)\n",
    "x1[0][0] / x1_sum[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "import os\n",
    "import openai\n",
    "from tenacity import retry, wait_random_exponential, stop_after_attempt, retry_if_not_exception_type\n",
    "\n",
    "client = OpenAI(api_key=\"sk-uRUknVzF2daRmidJ227fEa256c5d4f4b9f9a7bB6CeCd0176\", base_url=\"https://api.openai-hub.com/v1\")\n",
    "\n",
    "EMBEDDING_MODEL = 'text-embedding-3-small'\n",
    "EMBEDDING_CTX_LENGTH = 8191\n",
    "EMBEDDING_ENCODING = 'cl100k_base'\n",
    "\n",
    "# let's make sure to not retry on an invalid request, because that is what we want to demonstrate\n",
    "@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6), retry=retry_if_not_exception_type(openai.BadRequestError))\n",
    "def get_embedding(text_or_tokens, model=EMBEDDING_MODEL):\n",
    "    client_embed = client.embeddings.create(input=text_or_tokens, model=model)\n",
    "    print(\"get_embedding\", client_embed)\n",
    "    return client_embed.data[0].embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "BadRequestError",
     "evalue": "Error code: 400 - {'error': {'message': \"This model's maximum context length is 8192 tokens, however you requested 10001 tokens (10001 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\", 'type': 'invalid_request_error', 'param': '', 'code': None}}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mBadRequestError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m long_text \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAGI \u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m5000\u001b[39m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mget_embedding\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlong_text\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:330\u001b[0m, in \u001b[0;36mBaseRetrying.wraps.<locals>.wrapped_f\u001b[0;34m(*args, **kw)\u001b[0m\n\u001b[1;32m    326\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(\n\u001b[1;32m    327\u001b[0m     f, functools\u001b[38;5;241m.\u001b[39mWRAPPER_ASSIGNMENTS \u001b[38;5;241m+\u001b[39m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__defaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m__kwdefaults__\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    328\u001b[0m )\n\u001b[1;32m    329\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapped_f\u001b[39m(\u001b[38;5;241m*\u001b[39margs: t\u001b[38;5;241m.\u001b[39mAny, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw: t\u001b[38;5;241m.\u001b[39mAny) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m t\u001b[38;5;241m.\u001b[39mAny:\n\u001b[0;32m--> 330\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:467\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    465\u001b[0m retry_state \u001b[38;5;241m=\u001b[39m RetryCallState(retry_object\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, fn\u001b[38;5;241m=\u001b[39mfn, args\u001b[38;5;241m=\u001b[39margs, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[1;32m    466\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[0;32m--> 467\u001b[0m     do \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miter\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    468\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m    469\u001b[0m         \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:368\u001b[0m, in \u001b[0;36mBaseRetrying.iter\u001b[0;34m(self, retry_state)\u001b[0m\n\u001b[1;32m    366\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m action \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mactions:\n\u001b[0;32m--> 368\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43maction\u001b[49m\u001b[43m(\u001b[49m\u001b[43mretry_state\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    369\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:390\u001b[0m, in \u001b[0;36mBaseRetrying._post_retry_check_actions.<locals>.<lambda>\u001b[0;34m(rs)\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_post_retry_check_actions\u001b[39m(\u001b[38;5;28mself\u001b[39m, retry_state: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRetryCallState\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mis_explicit_retry \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miter_state\u001b[38;5;241m.\u001b[39mretry_run_result):\n\u001b[0;32m--> 390\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_add_action_func(\u001b[38;5;28;01mlambda\u001b[39;00m rs: \u001b[43mrs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moutcome\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    391\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    393\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mafter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:438\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    436\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CancelledError()\n\u001b[1;32m    437\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;241m==\u001b[39m FINISHED:\n\u001b[0;32m--> 438\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__get_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    440\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_condition\u001b[38;5;241m.\u001b[39mwait(timeout)\n\u001b[1;32m    442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_state \u001b[38;5;129;01min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n",
      "File \u001b[0;32m/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/concurrent/futures/_base.py:390\u001b[0m, in \u001b[0;36mFuture.__get_result\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception:\n\u001b[1;32m    389\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 390\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_exception\n\u001b[1;32m    391\u001b[0m     \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    392\u001b[0m         \u001b[38;5;66;03m# Break a reference cycle with the exception in self._exception\u001b[39;00m\n\u001b[1;32m    393\u001b[0m         \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/tenacity/__init__.py:470\u001b[0m, in \u001b[0;36mRetrying.__call__\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m    468\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(do, DoAttempt):\n\u001b[1;32m    469\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 470\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    471\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:  \u001b[38;5;66;03m# noqa: B902\u001b[39;00m\n\u001b[1;32m    472\u001b[0m         retry_state\u001b[38;5;241m.\u001b[39mset_exception(sys\u001b[38;5;241m.\u001b[39mexc_info())  \u001b[38;5;66;03m# type: ignore[arg-type]\u001b[39;00m\n",
      "Cell \u001b[0;32mIn[1], line 15\u001b[0m, in \u001b[0;36mget_embedding\u001b[0;34m(text_or_tokens, model)\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;129m@retry\u001b[39m(wait\u001b[38;5;241m=\u001b[39mwait_random_exponential(\u001b[38;5;28mmin\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mmax\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m), stop\u001b[38;5;241m=\u001b[39mstop_after_attempt(\u001b[38;5;241m6\u001b[39m), retry\u001b[38;5;241m=\u001b[39mretry_if_not_exception_type(openai\u001b[38;5;241m.\u001b[39mBadRequestError))\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_embedding\u001b[39m(text_or_tokens, model\u001b[38;5;241m=\u001b[39mEMBEDDING_MODEL):\n\u001b[0;32m---> 15\u001b[0m     client_embed \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43membeddings\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtext_or_tokens\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget_embedding\u001b[39m\u001b[38;5;124m\"\u001b[39m, client_embed)\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m client_embed\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39membedding\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/openai/resources/embeddings.py:114\u001b[0m, in \u001b[0;36mEmbeddings.create\u001b[0;34m(self, input, model, dimensions, encoding_format, user, extra_headers, extra_query, extra_body, timeout)\u001b[0m\n\u001b[1;32m    108\u001b[0m         embedding\u001b[38;5;241m.\u001b[39membedding \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mfrombuffer(  \u001b[38;5;66;03m# type: ignore[no-untyped-call]\u001b[39;00m\n\u001b[1;32m    109\u001b[0m             base64\u001b[38;5;241m.\u001b[39mb64decode(data), dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfloat32\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    110\u001b[0m         )\u001b[38;5;241m.\u001b[39mtolist()\n\u001b[1;32m    112\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_post\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    115\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m/embeddings\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    116\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbody\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmaybe_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mparams\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43membedding_create_params\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mEmbeddingCreateParams\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    117\u001b[0m \u001b[43m    \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmake_request_options\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    118\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextra_headers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_headers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    119\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextra_query\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_query\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    120\u001b[0m \u001b[43m        \u001b[49m\u001b[43mextra_body\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextra_body\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    121\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    122\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpost_parser\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparser\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    123\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    124\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mCreateEmbeddingResponse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    125\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/openai/_base_client.py:1240\u001b[0m, in \u001b[0;36mSyncAPIClient.post\u001b[0;34m(self, path, cast_to, body, options, files, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1226\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(\n\u001b[1;32m   1227\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   1228\u001b[0m     path: \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1235\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m   1236\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[1;32m   1237\u001b[0m     opts \u001b[38;5;241m=\u001b[39m FinalRequestOptions\u001b[38;5;241m.\u001b[39mconstruct(\n\u001b[1;32m   1238\u001b[0m         method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url\u001b[38;5;241m=\u001b[39mpath, json_data\u001b[38;5;241m=\u001b[39mbody, files\u001b[38;5;241m=\u001b[39mto_httpx_files(files), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions\n\u001b[1;32m   1239\u001b[0m     )\n\u001b[0;32m-> 1240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m cast(ResponseT, \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrequest\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mopts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m)\u001b[49m)\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/openai/_base_client.py:921\u001b[0m, in \u001b[0;36mSyncAPIClient.request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrequest\u001b[39m(\n\u001b[1;32m    913\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    914\u001b[0m     cast_to: Type[ResponseT],\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    919\u001b[0m     stream_cls: \u001b[38;5;28mtype\u001b[39m[_StreamT] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    920\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m ResponseT \u001b[38;5;241m|\u001b[39m _StreamT:\n\u001b[0;32m--> 921\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    922\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcast_to\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcast_to\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    923\u001b[0m \u001b[43m        \u001b[49m\u001b[43moptions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moptions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    924\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    925\u001b[0m \u001b[43m        \u001b[49m\u001b[43mstream_cls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream_cls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    926\u001b[0m \u001b[43m        \u001b[49m\u001b[43mremaining_retries\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mremaining_retries\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    927\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/openai/_base_client.py:1020\u001b[0m, in \u001b[0;36mSyncAPIClient._request\u001b[0;34m(self, cast_to, options, remaining_retries, stream, stream_cls)\u001b[0m\n\u001b[1;32m   1017\u001b[0m         err\u001b[38;5;241m.\u001b[39mresponse\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m   1019\u001b[0m     log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRe-raising status error\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 1020\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_status_error_from_response(err\u001b[38;5;241m.\u001b[39mresponse) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1022\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_response(\n\u001b[1;32m   1023\u001b[0m     cast_to\u001b[38;5;241m=\u001b[39mcast_to,\n\u001b[1;32m   1024\u001b[0m     options\u001b[38;5;241m=\u001b[39moptions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1027\u001b[0m     stream_cls\u001b[38;5;241m=\u001b[39mstream_cls,\n\u001b[1;32m   1028\u001b[0m )\n",
      "\u001b[0;31mBadRequestError\u001b[0m: Error code: 400 - {'error': {'message': \"This model's maximum context length is 8192 tokens, however you requested 10001 tokens (10001 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.\", 'type': 'invalid_request_error', 'param': '', 'code': None}}"
     ]
    }
   ],
   "source": [
    "long_text = 'AGI ' * 5000\n",
    "get_embedding(long_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tiktoken"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Encoding 'cl100k_base'>\n"
     ]
    }
   ],
   "source": [
    "encoding = tiktoken.encoding_for_model(\"gpt-3.5-turbo\")\n",
    "print(encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[83, 1609, 5963, 374, 2294, 0]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding.encode(\"tiktoken is great!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'tiktoken is great!'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoding.decode([83, 1609, 5963, 374, 2294, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[b't', b'ik', b'token', b' is', b' great', b'!']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[encoding.decode_single_token_bytes(token) for token in [83, 1609, 5963, 374, 2294, 0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compare_encodings(example_string: str) -> None:\n",
    "    \"\"\"Prints a comparison of three string encodings.\"\"\"\n",
    "    # print the example string\n",
    "    print(f'\\nExample string: \"{example_string}\"')\n",
    "    # for each encoding, print the # of tokens, the token integers, and the token bytes\n",
    "    for encoding_name in [\"r50k_base\", \"p50k_base\", \"cl100k_base\"]:\n",
    "        encoding = tiktoken.get_encoding(encoding_name)\n",
    "        token_integers = encoding.encode(example_string)\n",
    "        num_tokens = len(token_integers)\n",
    "        token_bytes = [encoding.decode_single_token_bytes(token) for token in token_integers]\n",
    "        print()\n",
    "        print(f\"{encoding_name}: {num_tokens} tokens\")\n",
    "        print(f\"token integers: {token_integers}\")\n",
    "        print(f\"token bytes: {token_bytes}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Example string: \"antidisestablishmentarianism\"\n",
      "\n",
      "r50k_base: 5 tokens\n",
      "token integers: [415, 29207, 44390, 3699, 1042]\n",
      "token bytes: [b'ant', b'idis', b'establishment', b'arian', b'ism']\n",
      "\n",
      "p50k_base: 5 tokens\n",
      "token integers: [415, 29207, 44390, 3699, 1042]\n",
      "token bytes: [b'ant', b'idis', b'establishment', b'arian', b'ism']\n",
      "\n",
      "cl100k_base: 6 tokens\n",
      "token integers: [519, 85342, 34500, 479, 8997, 2191]\n",
      "token bytes: [b'ant', b'idis', b'establish', b'ment', b'arian', b'ism']\n"
     ]
    }
   ],
   "source": [
    "compare_encodings(\"antidisestablishmentarianism\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Example string: \"2 + 2 = 4\"\n",
      "\n",
      "r50k_base: 5 tokens\n",
      "token integers: [17, 1343, 362, 796, 604]\n",
      "token bytes: [b'2', b' +', b' 2', b' =', b' 4']\n",
      "\n",
      "p50k_base: 5 tokens\n",
      "token integers: [17, 1343, 362, 796, 604]\n",
      "token bytes: [b'2', b' +', b' 2', b' =', b' 4']\n",
      "\n",
      "cl100k_base: 7 tokens\n",
      "token integers: [17, 489, 220, 17, 284, 220, 19]\n",
      "token bytes: [b'2', b' +', b' ', b'2', b' =', b' ', b'4']\n"
     ]
    }
   ],
   "source": [
    "compare_encodings(\"2 + 2 = 4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def truncate_text_tokens(text, encoding_name=EMBEDDING_ENCODING, max_tokens=EMBEDDING_CTX_LENGTH):\n",
    "    \"\"\"Truncate a string to have `max_tokens` according to the given encoding.\"\"\"\n",
    "    encoding = tiktoken.get_encoding(encoding_name)\n",
    "    return encoding.encode(text)[:max_tokens]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "data": {
      "text/plain": [
       "1536"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncated = truncate_text_tokens(long_text)\n",
    "print(len(truncated), truncated)\n",
    "len(get_embedding(truncated))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import islice\n",
    "\n",
    "def batched(iterable, n):\n",
    "    \"\"\"Batch data into tuples of length n. The last batch may be shorter.\"\"\"\n",
    "    # batched('ABCDEFG', 3) --> ABC DEF G\n",
    "    if n < 1:\n",
    "        raise ValueError('n must be at least one')\n",
    "    it = iter(iterable)\n",
    "    while (batch := tuple(islice(it, n))):\n",
    "        yield batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<generator object batched at 0x11f242510>\n",
      "('B', 'a', 't', 'c', 'h')\n",
      "(' ', 'd', 'a', 't', 'a')\n",
      "(' ', 'i', 'n', 't', 'o')\n",
      "(' ', 't', 'u', 'p', 'l')\n",
      "('e', 's', ' ', 'o', 'f')\n",
      "(' ', 'l', 'e', 'n', 'g')\n",
      "('t', 'h', ' ', 'n', '.')\n",
      "(' ', 'T', 'h', 'e', ' ')\n",
      "('l', 'a', 's', 't', ' ')\n",
      "('b', 'a', 't', 'c', 'h')\n",
      "(' ', 'm', 'a', 'y', ' ')\n",
      "('b', 'e', ' ', 's', 'h')\n",
      "('o', 'r', 't', 'e', 'r')\n",
      "('.',)\n"
     ]
    }
   ],
   "source": [
    "#tokens_m = [\"Batch data into tuples of length n. The last batch may be shorter.\"]\n",
    "tokens_m = \"Batch data into tuples of length n. The last batch may be shorter.\"\n",
    "chunks_iterator = batched(tokens_m, 5)\n",
    "print(chunks_iterator)\n",
    "for k in chunks_iterator:\n",
    "    print(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def chunked_tokens(text, encoding_name, chunk_length):\n",
    "    encoding = tiktoken.get_encoding(encoding_name)\n",
    "    tokens = encoding.encode(text)\n",
    "    chunks_iterator = batched(tokens, chunk_length)\n",
    "    yield from chunks_iterator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "def len_safe_get_embedding(text, model=EMBEDDING_MODEL, max_tokens=EMBEDDING_CTX_LENGTH, encoding_name=EMBEDDING_ENCODING, average=True):\n",
    "    chunk_embeddings = []\n",
    "    chunk_lens = []\n",
    "    for chunk in chunked_tokens(text, encoding_name=encoding_name, chunk_length=max_tokens):\n",
    "        chunk_embeddings.append(get_embedding(chunk, model=model))\n",
    "        chunk_lens.append(len(chunk))\n",
    "\n",
    "    if average:\n",
    "        chunk_embeddings = np.average(chunk_embeddings, axis=0, weights=chunk_lens)\n",
    "        chunk_embeddings = chunk_embeddings / np.linalg.norm(chunk_embeddings)  # normalizes length to 1\n",
    "        chunk_embeddings = chunk_embeddings.tolist()\n",
    "    return chunk_embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "get_embedding CreateEmbeddingResponse(data=[Embedding(embedding=[-0.01966564543545246, 0.03236118704080582, -0.027382543310523033, 0.018452100455760956, 0.020516162738204002, 0.012529588304460049, 0.015859056264162064, -0.02227943390607834, -0.02398047037422657, -0.009319399483501911, -0.00548169482499361, -0.02410493604838848, -0.014085414819419384, -0.00883190706372261, -0.016968879848718643, 0.00529240258038044, -0.022341666743159294, -0.06285538524389267, -0.01467662863433361, 0.02248687669634819, 0.02026723138988018, 0.03331542760133743, -0.019779738038778305, 0.037153132259845734, -0.02530810795724392, -0.04219400882720947, 0.018877359107136726, 0.0022053837310522795, -0.005450578406453133, 0.03976692259311676, 0.053147029131650925, -0.02595118246972561, -0.0033113169483840466, -0.06974250823259354, -0.006352957338094711, 0.028316039592027664, -0.0325271412730217, -0.0031168386340141296, 0.022673575207591057, -0.01388834323734045, 0.06285538524389267, 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      "get_embedding CreateEmbeddingResponse(data=[Embedding(embedding=[-0.025586608797311783, 0.04110307618975639, -0.02729831077158451, 0.018150709569454193, 0.02889886312186718, 0.01561650075018406, 0.016094444319605827, -0.01737266220152378, -0.018784260377287865, -0.0020187522750347853, -0.0014560581184923649, -0.013915914110839367, -0.012693270109593868, -0.004573800601065159, -0.01067034900188446, -0.004432085435837507, -0.02540876902639866, -0.07006862759590149, -0.017050329595804214, 0.013660269789397717, 0.03458971530199051, 0.04232572019100189, -0.027431689202785492, 0.031544219702482224, -0.030699484050273895, -0.0389690063893795, 0.013560235500335693, 0.0021646360401064157, -0.005796445067971945, 0.034789785742759705, 0.06024301424622536, -0.03732399269938469, -0.0048322235234081745, -0.0637553408741951, -0.003387280274182558, 0.031677599996328354, -0.032188888639211655, -0.010453607887029648, 0.02231881394982338, -0.0130822928622365, 0.05615271255373955, 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      "Setting average=True gives us a single 1536-dimensional embedding vector for our long text.\n",
      "Setting average=False gives us 2 embedding vectors, one for each of the chunks.\n"
     ]
    }
   ],
   "source": [
    "average_embedding_vector = len_safe_get_embedding(long_text, average=True)\n",
    "chunks_embedding_vectors = len_safe_get_embedding(long_text, average=False)\n",
    "\n",
    "print(f\"Setting average=True gives us a single {len(average_embedding_vector)}-dimensional embedding vector for our long text.\")\n",
    "print(f\"Setting average=False gives us {len(chunks_embedding_vectors)} embedding vectors, one for each of the chunks.\")\n"
   ]
  }
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